Choosing Prior Hyperparameters: With Applications to Time-Varying Parameter Models
针对时变参数模型中的先验超参数选择问题，提出一种快速、易实现的完全贝叶斯方法，将超参数作为层次模型的一部分与其他参数联合估计，蒙特卡洛模拟表明该方法优于固定超参数的做法。
Time-varying parameter models with stochastic volatility are widely used to study macroeconomic and financial data. These models are almost exclusively estimated using Bayesian methods. A common practice is to focus on prior distributions that themselves depend on relatively few hyperparameters such as the scaling factor for the prior covariance matrix of the residuals governing time variation in the parameters. The choice of these hyperparameters is crucial because their influence is sizeable for standard sample sizes. In this paper we treat the hyperparameters as part of a hierarchical model and propose a fast, tractable, easy-to-implement, and fully Bayesian approach to estimate those hyperparameters jointly with all other parameters in the model. We show via Monte Carlo simulations that, in this class of models, our approach can drastically improve on using fixed hyperparameters previously proposed in the literature.